{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2bvkQcUOvP5Q"
   },
   "source": [
    "# Logistic regression example\n",
    "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
    "\n",
    "---\n",
    "\n",
    "This notebook demonstrates solving a logistic regression problem of predicting Hypothyrodism with **Scikit-learn** and **Statsmodels** libraries.\n",
    "\n",
    "The dataset is taken from UCI ML repository.\n",
    "<br>Here is the link: https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gBvhbvQ3vP5T"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "caPnVuIWvP5a"
   },
   "source": [
    "### Read the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UBqpn2sFvP5b"
   },
   "outputs": [],
   "source": [
    "names = 'response age sex on_thyroxine query_on_thyroxine antithyroid_medication thyroid_surgery query_hypothyroid query_hyperthyroid pregnant \\\n",
    "sick tumor lithium goitre TSH_measured TSH T3_measured \\\n",
    "T3 TT4_measured TT4 T4U_measured T4U FTI_measured FTI TBG_measured TBG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KU4-8YGYvP5f"
   },
   "outputs": [],
   "source": [
    "names = names.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "qSTybVWUwsKq",
    "outputId": "ea0c2026-0507-4be2-a2d0-7f7f34ffcc91"
   },
   "outputs": [],
   "source": [
    "#!wget https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Datasets/hypothyroid.csv\n",
    "\n",
    "#!mkdir Data\n",
    "#!mv hypothyroid.csv Data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hQxY_7FHvP5i"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/hypothyroid.csv',index_col=False,names=names,na_values=['?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "id": "IIG7QEoOvP5l",
    "outputId": "c67b69ff-2f7c-4d3b-e6eb-e7b54ea13b63"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>on_thyroxine</th>\n",
       "      <th>query_on_thyroxine</th>\n",
       "      <th>antithyroid_medication</th>\n",
       "      <th>thyroid_surgery</th>\n",
       "      <th>query_hypothyroid</th>\n",
       "      <th>query_hyperthyroid</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>...</th>\n",
       "      <th>T3_measured</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4_measured</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U_measured</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI_measured</th>\n",
       "      <th>FTI</th>\n",
       "      <th>TBG_measured</th>\n",
       "      <th>TBG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>72.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.6</td>\n",
       "      <td>y</td>\n",
       "      <td>15.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.48</td>\n",
       "      <td>y</td>\n",
       "      <td>10.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>1.7</td>\n",
       "      <td>y</td>\n",
       "      <td>19.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.13</td>\n",
       "      <td>y</td>\n",
       "      <td>17.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.2</td>\n",
       "      <td>y</td>\n",
       "      <td>4.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.00</td>\n",
       "      <td>y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.4</td>\n",
       "      <td>y</td>\n",
       "      <td>6.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.04</td>\n",
       "      <td>y</td>\n",
       "      <td>6.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>77.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>y</td>\n",
       "      <td>57.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.28</td>\n",
       "      <td>y</td>\n",
       "      <td>44.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      response   age sex on_thyroxine query_on_thyroxine  \\\n",
       "0  hypothyroid  72.0   M            f                  f   \n",
       "1  hypothyroid  15.0   F            t                  f   \n",
       "2  hypothyroid  24.0   M            f                  f   \n",
       "3  hypothyroid  24.0   F            f                  f   \n",
       "4  hypothyroid  77.0   M            f                  f   \n",
       "\n",
       "  antithyroid_medication thyroid_surgery query_hypothyroid query_hyperthyroid  \\\n",
       "0                      f               f                 f                  f   \n",
       "1                      f               f                 f                  f   \n",
       "2                      f               f                 f                  f   \n",
       "3                      f               f                 f                  f   \n",
       "4                      f               f                 f                  f   \n",
       "\n",
       "  pregnant ...  T3_measured   T3 TT4_measured   TT4 T4U_measured   T4U  \\\n",
       "0        f ...            y  0.6            y  15.0            y  1.48   \n",
       "1        f ...            y  1.7            y  19.0            y  1.13   \n",
       "2        f ...            y  0.2            y   4.0            y  1.00   \n",
       "3        f ...            y  0.4            y   6.0            y  1.04   \n",
       "4        f ...            y  1.2            y  57.0            y  1.28   \n",
       "\n",
       "  FTI_measured   FTI TBG_measured  TBG  \n",
       "0            y  10.0            n  NaN  \n",
       "1            y  17.0            n  NaN  \n",
       "2            y   0.0            n  NaN  \n",
       "3            y   6.0            n  NaN  \n",
       "4            y  44.0            n  NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PDof5gIovP5s"
   },
   "outputs": [],
   "source": [
    "to_drop=[]\n",
    "for c in df.columns:\n",
    "    if 'measured' in c or 'query' in c:\n",
    "        to_drop.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "id": "xyXfEueTvP50",
    "outputId": "b5f9fdf3-ee47-4d56-f9fc-1561ac4d44d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['query_on_thyroxine',\n",
       " 'query_hypothyroid',\n",
       " 'query_hyperthyroid',\n",
       " 'TSH_measured',\n",
       " 'T3_measured',\n",
       " 'TT4_measured',\n",
       " 'T4U_measured',\n",
       " 'FTI_measured',\n",
       " 'TBG_measured']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VZ0DHD8_vP54"
   },
   "outputs": [],
   "source": [
    "to_drop.append('TBG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HHWwfBR-vP59"
   },
   "outputs": [],
   "source": [
    "df.drop(to_drop,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "2iFMV-9TvP6C",
    "outputId": "ed3c5277-a40d-49e9-ad5b-851e713ab09a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>on_thyroxine</th>\n",
       "      <th>antithyroid_medication</th>\n",
       "      <th>thyroid_surgery</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>sick</th>\n",
       "      <th>tumor</th>\n",
       "      <th>lithium</th>\n",
       "      <th>goitre</th>\n",
       "      <th>TSH</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>72.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>430.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>77.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      response   age sex on_thyroxine antithyroid_medication thyroid_surgery  \\\n",
       "0  hypothyroid  72.0   M            f                      f               f   \n",
       "1  hypothyroid  15.0   F            t                      f               f   \n",
       "2  hypothyroid  24.0   M            f                      f               f   \n",
       "3  hypothyroid  24.0   F            f                      f               f   \n",
       "4  hypothyroid  77.0   M            f                      f               f   \n",
       "\n",
       "  pregnant sick tumor lithium goitre    TSH   T3   TT4   T4U   FTI  \n",
       "0        f    f     f       f      f   30.0  0.6  15.0  1.48  10.0  \n",
       "1        f    f     f       f      f  145.0  1.7  19.0  1.13  17.0  \n",
       "2        f    f     f       f      f    0.0  0.2   4.0  1.00   0.0  \n",
       "3        f    f     f       f      f  430.0  0.4   6.0  1.04   6.0  \n",
       "4        f    f     f       f      f    7.3  1.2  57.0  1.28  44.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ALZwMuCpvP6J"
   },
   "source": [
    "### Let us see the basic statistics on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "4aXISwuJvP6K",
    "outputId": "71e4f700-0e12-4f65-adb6-8c6ab613d484"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>2717.0</td>\n",
       "      <td>51.154214</td>\n",
       "      <td>19.294405</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>67.000</td>\n",
       "      <td>98.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSH</th>\n",
       "      <td>2695.0</td>\n",
       "      <td>5.923180</td>\n",
       "      <td>23.899467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.300</td>\n",
       "      <td>530.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>2468.0</td>\n",
       "      <td>1.939749</td>\n",
       "      <td>0.996773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.300</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TT4</th>\n",
       "      <td>2914.0</td>\n",
       "      <td>108.850000</td>\n",
       "      <td>45.485419</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>128.000</td>\n",
       "      <td>450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4U</th>\n",
       "      <td>2915.0</td>\n",
       "      <td>0.978199</td>\n",
       "      <td>0.226580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.065</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTI</th>\n",
       "      <td>2916.0</td>\n",
       "      <td>115.397771</td>\n",
       "      <td>60.239572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>129.000</td>\n",
       "      <td>881.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count        mean        std  min    25%     50%      75%     max\n",
       "age  2717.0   51.154214  19.294405  1.0  35.00   54.00   67.000   98.00\n",
       "TSH  2695.0    5.923180  23.899467  0.0   0.00    0.70    2.300  530.00\n",
       "T3   2468.0    1.939749   0.996773  0.0   1.40    1.80    2.300   10.20\n",
       "TT4  2914.0  108.850000  45.485419  2.0  83.00  104.00  128.000  450.00\n",
       "T4U  2915.0    0.978199   0.226580  0.0   0.85    0.96    1.065    2.21\n",
       "FTI  2916.0  115.397771  60.239572  0.0  91.00  107.00  129.000  881.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bGTF9UDpvP6U"
   },
   "source": [
    "### Are any data points are missing? We can check it using `df.isna()` method\n",
    "The `df.isna()` method gives back a full DataFrame with Boolean values - True for data present, False for missing data. We can use `sum()` on that DataFrame to see and calculate the number of missing values per column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "colab_type": "code",
    "id": "WKbQCqf_vP6X",
    "outputId": "533a5442-e601-4fb8-fc1e-288394100dd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "response                    0\n",
       "age                       446\n",
       "sex                        73\n",
       "on_thyroxine                0\n",
       "antithyroid_medication      0\n",
       "thyroid_surgery             0\n",
       "pregnant                    0\n",
       "sick                        0\n",
       "tumor                       0\n",
       "lithium                     0\n",
       "goitre                      0\n",
       "TSH                       468\n",
       "T3                        695\n",
       "TT4                       249\n",
       "T4U                       248\n",
       "FTI                       247\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tNhUJwc4vP6b"
   },
   "source": [
    "### We can use `df.dropna()` method to drop those missing rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0TK5dwbGvP6c"
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "YIQLA-SDvP6f",
    "outputId": "137bc7b2-0d54-45da-e4af-feb9ba16ac2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 16)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KjqJoWCYvP6i"
   },
   "source": [
    "### Creating a transformation function to convert `+` or `-` responses to 1 and 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "prRGSuntvP6k"
   },
   "outputs": [],
   "source": [
    "def class_convert(response):\n",
    "    if response=='hypothyroid':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vhZRrgMCvP6s"
   },
   "outputs": [],
   "source": [
    "df['response']=df['response'].apply(class_convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "oMvub8wHvP6v",
    "outputId": "d488da5d-b3a2-419e-89c8-98a5c8b7ccf7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>on_thyroxine</th>\n",
       "      <th>antithyroid_medication</th>\n",
       "      <th>thyroid_surgery</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>sick</th>\n",
       "      <th>tumor</th>\n",
       "      <th>lithium</th>\n",
       "      <th>goitre</th>\n",
       "      <th>TSH</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>430.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>77.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   response   age sex on_thyroxine antithyroid_medication thyroid_surgery  \\\n",
       "0         1  72.0   M            f                      f               f   \n",
       "1         1  15.0   F            t                      f               f   \n",
       "2         1  24.0   M            f                      f               f   \n",
       "3         1  24.0   F            f                      f               f   \n",
       "4         1  77.0   M            f                      f               f   \n",
       "\n",
       "  pregnant sick tumor lithium goitre    TSH   T3   TT4   T4U   FTI  \n",
       "0        f    f     f       f      f   30.0  0.6  15.0  1.48  10.0  \n",
       "1        f    f     f       f      f  145.0  1.7  19.0  1.13  17.0  \n",
       "2        f    f     f       f      f    0.0  0.2   4.0  1.00   0.0  \n",
       "3        f    f     f       f      f  430.0  0.4   6.0  1.04   6.0  \n",
       "4        f    f     f       f      f    7.3  1.2  57.0  1.28  44.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "TSg0NNwZvP62",
    "outputId": "44b18462-2666-43e3-9e2f-e5bb986b6f3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['response', 'age', 'sex', 'on_thyroxine', 'antithyroid_medication',\n",
       "       'thyroid_surgery', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre',\n",
       "       'TSH', 'T3', 'TT4', 'T4U', 'FTI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XQPbkEhSvP69"
   },
   "source": [
    "### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "PKC4kJgivP7C",
    "outputId": "045f855a-e1b7-4538-be55-8fbae2e88fdf"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for var in ['age','TSH','T3','TT4','T4U','FTI']:\n",
    "    sns.boxplot(x='response',y=var,data=df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Pe0sLCwvvP7P",
    "outputId": "07b02f1d-5b8e-4f1e-f76d-09f52f3318ad"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df[df.columns[1:]],diag_kws={'edgecolor':'k','bins':25},plot_kws={'edgecolor':'k'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kkUKMvPgEUFW"
   },
   "source": [
    "### Create dummy variables for the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cmnKaugfvP7T"
   },
   "outputs": [],
   "source": [
    "df_dummies = pd.get_dummies(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6LPTF7VCvP7Y",
    "outputId": "cb56c0fb-596c-4565-aa46-006a28a1e8ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 25)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "colab_type": "code",
    "id": "Cn3vcIoWvP7c",
    "outputId": "a2449ffd-e8f2-4905-ad6a-d3a4bf13b0ec"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>TSH</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI</th>\n",
       "      <th>sex_F</th>\n",
       "      <th>sex_M</th>\n",
       "      <th>on_thyroxine_f</th>\n",
       "      <th>...</th>\n",
       "      <th>pregnant_f</th>\n",
       "      <th>pregnant_t</th>\n",
       "      <th>sick_f</th>\n",
       "      <th>sick_t</th>\n",
       "      <th>tumor_f</th>\n",
       "      <th>tumor_t</th>\n",
       "      <th>lithium_f</th>\n",
       "      <th>lithium_t</th>\n",
       "      <th>goitre_f</th>\n",
       "      <th>goitre_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.24</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2803</th>\n",
       "      <td>0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>116.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2779</th>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>113.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.18</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.92</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.21</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.06</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      response   age   TSH   T3    TT4   T4U    FTI  sex_F  sex_M  \\\n",
       "678          0  76.0   0.0  2.6  149.0  1.24  120.0      1      0   \n",
       "2803         0  65.0   0.0  0.9   78.0  0.67  116.0      0      1   \n",
       "1404         0  71.0   3.1  1.6   87.0  0.98   89.0      1      0   \n",
       "2779         0  63.0   0.4  1.4  115.0  1.02  113.0      0      1   \n",
       "2949         0  70.0   1.6  1.8  175.0  1.18  148.0      1      0   \n",
       "1487         0  60.0   0.9  1.4  138.0  0.92  150.0      1      0   \n",
       "1410         0  32.0  17.4  1.9   90.0  1.21   74.0      1      0   \n",
       "1700         0  78.0   6.3  1.1   56.0  0.86   65.0      1      0   \n",
       "1131         0  77.0   3.8  0.5   54.0  0.58   93.0      1      0   \n",
       "577          0  28.0   0.8  1.9   85.0  1.06   80.0      1      0   \n",
       "\n",
       "      on_thyroxine_f    ...     pregnant_f  pregnant_t  sick_f  sick_t  \\\n",
       "678                1    ...              1           0       1       0   \n",
       "2803               1    ...              1           0       1       0   \n",
       "1404               1    ...              1           0       1       0   \n",
       "2779               1    ...              1           0       1       0   \n",
       "2949               0    ...              1           0       1       0   \n",
       "1487               0    ...              1           0       1       0   \n",
       "1410               1    ...              1           0       1       0   \n",
       "1700               1    ...              1           0       1       0   \n",
       "1131               1    ...              1           0       0       1   \n",
       "577                1    ...              1           0       1       0   \n",
       "\n",
       "      tumor_f  tumor_t  lithium_f  lithium_t  goitre_f  goitre_t  \n",
       "678         1        0          1          0         1         0  \n",
       "2803        1        0          1          0         1         0  \n",
       "1404        1        0          1          0         1         0  \n",
       "2779        1        0          1          0         1         0  \n",
       "2949        1        0          1          0         1         0  \n",
       "1487        1        0          1          0         1         0  \n",
       "1410        1        0          1          0         1         0  \n",
       "1700        1        0          1          0         1         0  \n",
       "1131        1        0          1          0         1         0  \n",
       "577         0        1          1          0         1         0  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cOtuodYBvP7g"
   },
   "source": [
    "### Test/train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tknuBZDnvP7h"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_dummies.drop('response',axis=1), \n",
    "                                                    df_dummies['response'], test_size=0.30, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "G2RA5KJcEe1j",
    "outputId": "259d0964-d42f-4cbd-b28a-bde1bee22c8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape (1400, 24)\n",
      "Test set shape (600, 24)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set shape\",X_train.shape)\n",
    "print(\"Test set shape\",X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "udN1_fDGvP7j"
   },
   "source": [
    "### Using `LogisticRegression` estimator from Scikit-learn\n",
    "We are using the L2 regularization by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P62kZToSvP7k"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GGqsx2JQvP7n"
   },
   "outputs": [],
   "source": [
    "clf1 = LogisticRegression(penalty='l2',solver='newton-cg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "id": "OKvgysYbvP7r",
    "outputId": "bbc91a6c-0de0-4375-980a-983e1b87eaf1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='newton-cg',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "92jXObyGEtqv"
   },
   "source": [
    "### Intercept, coefficients, and score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cE5kZZfKvP7u",
    "outputId": "baa40039-6f76-4d2a-c8c9-1c224ae17e60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.28510517])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "id": "Tl_QpokTvP70",
    "outputId": "50bf756a-def9-42ca-8980-2eab9093443f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0203188 ,  0.01861475, -0.32522082,  0.00395531,  0.30700632,\n",
       "        -0.10796216,  0.30204065, -0.30204118,  0.89772448, -0.89772501,\n",
       "         0.62687937, -0.6268799 , -0.67443716,  0.67443663,  0.13427707,\n",
       "        -0.1342776 ,  0.27312666, -0.27312719,  0.26138707, -0.2613876 ,\n",
       "         0.08932434, -0.08932487, -0.24898789,  0.24898736]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "JNLVBLeBvP75",
    "outputId": "a3d00ed7-9d23-4661-8492-5333f2fb76bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9816666666666667"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bcd0se4pvP7_"
   },
   "source": [
    "### For `LogisticRegression` estimator, there is a special `predict_proba` method which computes the raw probability values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x-bfhAPhFhC-"
   },
   "outputs": [],
   "source": [
    "prob_threshold = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "colab_type": "code",
    "id": "iyhdw03vvP8A",
    "outputId": "5a3c2363-1573-44cf-edce-c3dedce3de95"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prob of NO</th>\n",
       "      <th>Prob of YES</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.990471</td>\n",
       "      <td>0.009529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.998714</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.624018</td>\n",
       "      <td>0.375982</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.999296</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.864075</td>\n",
       "      <td>0.135925</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.983696</td>\n",
       "      <td>0.016304</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.998507</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.221967</td>\n",
       "      <td>0.778033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prob of NO  Prob of YES  Decision\n",
       "0    0.990471     0.009529         0\n",
       "1    0.998714     0.001286         0\n",
       "2    0.999999     0.000001         0\n",
       "3    0.624018     0.375982         0\n",
       "4    0.999296     0.000704         0\n",
       "5    0.864075     0.135925         0\n",
       "6    0.983696     0.016304         0\n",
       "7    0.998507     0.001493         0\n",
       "8    0.221967     0.778033         1\n",
       "9    0.999968     0.000032         0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_df=pd.DataFrame(clf1.predict_proba(X_test[:10]),columns=['Prob of NO','Prob of YES'])\n",
    "prob_df['Decision']=(prob_df['Prob of YES']>prob_threshold).apply(int)\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "A4SQ1XXxvP8E",
    "outputId": "4a0e9218-7d24-473a-eed3-d246ee4d03ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2944    0\n",
       "511     0\n",
       "2116    0\n",
       "1412    0\n",
       "2039    0\n",
       "2013    0\n",
       "1478    0\n",
       "2744    0\n",
       "83      1\n",
       "2100    0\n",
       "Name: response, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_PeE-kOIvP8J"
   },
   "source": [
    "### Classification report, and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DqHMkTpfvP8K"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "id": "7Qv9q7vZvP8P",
    "outputId": "047e39c0-8b67-4dbc-f7e1-d23fba68b82d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       569\n",
      "           1       0.86      0.77      0.81        31\n",
      "\n",
      "   micro avg       0.98      0.98      0.98       600\n",
      "   macro avg       0.92      0.88      0.90       600\n",
      "weighted avg       0.98      0.98      0.98       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, clf1.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "XSNxqNpHvP8a",
    "outputId": "ae951eda-bed2-431e-bba1-2da0f7737f6f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predict-YES</th>\n",
       "      <th>Predict-NO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>565</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predict-YES  Predict-NO\n",
       "YES          565           4\n",
       "NO             7          24"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, clf1.predict(X_test)),columns=['Predict-YES','Predict-NO'],index=['YES','NO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "feE3XMmFvP8g"
   },
   "source": [
    "### Using `statsmodels` library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PPb2I3nUvP8i"
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "aSDnuENRvP8p",
    "outputId": "2c90eed3-ffd9-478c-8dcc-6a4751b03dea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['response', 'age', 'TSH', 'T3', 'TT4', 'T4U', 'FTI', 'sex_F', 'sex_M',\n",
       "       'on_thyroxine_f', 'on_thyroxine_t', 'antithyroid_medication_f',\n",
       "       'antithyroid_medication_t', 'thyroid_surgery_f', 'thyroid_surgery_t',\n",
       "       'pregnant_f', 'pregnant_t', 'sick_f', 'sick_t', 'tumor_f', 'tumor_t',\n",
       "       'lithium_f', 'lithium_t', 'goitre_f', 'goitre_t'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KtujrgvyvP8u"
   },
   "source": [
    "### Create a 'formula' in the same style as in R language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vIC8zIl7vP8v"
   },
   "outputs": [],
   "source": [
    "formula = 'response ~ ' + '+'.join(df_dummies.columns[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "Hiyjw4g7vP8z",
    "outputId": "8d46f0d2-00fa-472c-abd1-839f4304b94e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'response ~ age+TSH+T3+TT4+T4U+FTI+sex_F+sex_M+on_thyroxine_f+on_thyroxine_t+antithyroid_medication_f+antithyroid_medication_t+thyroid_surgery_f+thyroid_surgery_t+pregnant_f+pregnant_t+sick_f+sick_t+tumor_f+tumor_t+lithium_f+lithium_t+goitre_f+goitre_t'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_A9t8JJZvP86"
   },
   "source": [
    "### Fit a GLM (Generalized Linear model) with this formula and choosing `Binomial` as the family of function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k5Oi9XTQvP87"
   },
   "outputs": [],
   "source": [
    "model = smf.glm(formula = formula, data=df_dummies, family=sm.families.Binomial())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dWlUR6aQvP9C"
   },
   "outputs": [],
   "source": [
    "result=model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q-yoBbVUvP9M"
   },
   "source": [
    "### `summary` method shows a R-style table with all kind of statistical information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 689
    },
    "colab_type": "code",
    "id": "1pO4jVRhvP9N",
    "outputId": "9888a968-7d1d-4ede-95ae-481ecc751831"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:               response   No. Observations:                 2000\n",
      "Model:                            GLM   Df Residuals:                     1984\n",
      "Model Family:                Binomial   Df Model:                           15\n",
      "Link Function:                  logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -123.59\n",
      "Date:                Wed, 21 Aug 2019   Deviance:                       247.17\n",
      "Time:                        14:54:43   Pearson chi2:                 9.10e+03\n",
      "No. Iterations:                    22   Covariance Type:             nonrobust\n",
      "============================================================================================\n",
      "                               coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   -3.1161   4981.804     -0.001      1.000   -9767.273    9761.041\n",
      "age                          0.0219      0.011      2.062      0.039       0.001       0.043\n",
      "TSH                          0.0214      0.008      2.823      0.005       0.007       0.036\n",
      "T3                          -0.0233      0.320     -0.073      0.942      -0.651       0.604\n",
      "TT4                          0.0367      0.027      1.376      0.169      -0.016       0.089\n",
      "T4U                         -1.5693      1.656     -0.947      0.343      -4.816       1.677\n",
      "FTI                         -0.1420      0.030     -4.709      0.000      -0.201      -0.083\n",
      "sex_F                       -1.4005   2490.902     -0.001      1.000   -4883.479    4880.678\n",
      "sex_M                       -1.7156   2490.902     -0.001      0.999   -4883.794    4880.363\n",
      "on_thyroxine_f              -1.2357   2490.902     -0.000      1.000   -4883.314    4880.843\n",
      "on_thyroxine_t              -1.8804   2490.902     -0.001      0.999   -4883.959    4880.198\n",
      "antithyroid_medication_f    -0.3206   2490.902     -0.000      1.000   -4882.399    4881.758\n",
      "antithyroid_medication_t    -2.7954   2490.902     -0.001      0.999   -4884.874    4879.283\n",
      "thyroid_surgery_f           -2.2489   2490.902     -0.001      0.999   -4884.328    4879.830\n",
      "thyroid_surgery_t           -0.8672   2490.902     -0.000      1.000   -4882.946    4881.211\n",
      "pregnant_f                  -0.2489   2490.903  -9.99e-05      1.000   -4882.328    4881.830\n",
      "pregnant_t                  -2.8671   2490.903     -0.001      0.999   -4884.947    4879.213\n",
      "sick_f                      -1.1899   2490.902     -0.000      1.000   -4883.269    4880.889\n",
      "sick_t                      -1.9262   2490.902     -0.001      0.999   -4884.005    4880.153\n",
      "tumor_f                      8.1294   6275.938      0.001      0.999   -1.23e+04    1.23e+04\n",
      "tumor_t                    -11.2454   7357.670     -0.002      0.999   -1.44e+04    1.44e+04\n",
      "lithium_f                    8.7704   2.42e+04      0.000      1.000   -4.75e+04    4.75e+04\n",
      "lithium_t                  -11.8864   2.91e+04     -0.000      1.000    -5.7e+04     5.7e+04\n",
      "goitre_f                    -1.9841   2490.902     -0.001      0.999   -4884.063    4880.095\n",
      "goitre_t                    -1.1320   2490.902     -0.000      1.000   -4883.211    4880.947\n",
      "============================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RauDe86hvP9U"
   },
   "source": [
    "### The `predict` method computes probability for the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "woKRF8eSvP9V",
    "outputId": "d22928b5-8b1d-4e81-cb3c-3e8ca04f777e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2944    2.408353e-03\n",
       "511     6.250161e-04\n",
       "2116    6.125699e-07\n",
       "1412    3.274420e-01\n",
       "2039    7.888963e-04\n",
       "2013    1.325293e-01\n",
       "1478    3.197375e-02\n",
       "2744    1.098108e-03\n",
       "83      8.650453e-01\n",
       "2100    6.524129e-05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.predict(X_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O8nEHxDBvP9d"
   },
   "source": [
    "### To create binary predictions, you have to apply a threshold probability and convert the booleans into integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NAuhv8qivP9i"
   },
   "outputs": [],
   "source": [
    "y_pred=(result.predict(X_test)>prob_threshold).apply(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "id": "AbETGp2VvP9l",
    "outputId": "2dd64113-0ef5-4b15-90fc-901bd8d3ed93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       569\n",
      "           1       0.89      0.77      0.83        31\n",
      "\n",
      "   micro avg       0.98      0.98      0.98       600\n",
      "   macro avg       0.94      0.88      0.91       600\n",
      "weighted avg       0.98      0.98      0.98       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "uMxjluvgvP9u",
    "outputId": "63d11ef5-1f15-47cd-fe00-7ad2f98d2301"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predict-YES</th>\n",
       "      <th>Predict-NO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>566</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predict-YES  Predict-NO\n",
       "YES          566           3\n",
       "NO             7          24"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, y_pred),columns=['Predict-YES','Predict-NO'],index=['YES','NO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "60jxAabLvP9y"
   },
   "source": [
    "### A smaller model with only first few variables\n",
    "\n",
    "We saw that majority of variables in the logistic regression model has p-values very high and therefore they are not statistically significant. We create another smaller model removing those variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dJW9zPjWvP90",
    "outputId": "748434e1-a1c9-4e39-bfec-0903bb5ffecf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'response ~ age+TSH+T3+TT4+T4U+FTI'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'response ~ ' + '+'.join(df_dummies.columns[1:7])\n",
    "formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "colab_type": "code",
    "id": "Ujsp2wTdvP93",
    "outputId": "10a0bb28-7fc2-4fb5-fc01-dfc895b9cf1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:               response   No. Observations:                 2000\n",
      "Model:                            GLM   Df Residuals:                     1993\n",
      "Model Family:                Binomial   Df Model:                            6\n",
      "Link Function:                  logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -131.55\n",
      "Date:                Wed, 21 Aug 2019   Deviance:                       263.09\n",
      "Time:                        14:54:43   Pearson chi2:                 5.65e+03\n",
      "No. Iterations:                     9   Covariance Type:             nonrobust\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.1596      1.995      2.085      0.037       0.250       8.069\n",
      "age            0.0248      0.010      2.481      0.013       0.005       0.044\n",
      "TSH            0.0212      0.007      2.892      0.004       0.007       0.035\n",
      "T3             0.0704      0.297      0.237      0.812      -0.511       0.652\n",
      "TT4            0.0296      0.026      1.134      0.257      -0.022       0.081\n",
      "T4U           -1.2903      1.665     -0.775      0.438      -4.553       1.973\n",
      "FTI           -0.1307      0.029     -4.449      0.000      -0.188      -0.073\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "model = smf.glm(formula = formula, data=df_dummies, family=sm.families.Binomial())\n",
    "result=model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "id": "BFhjN31yvP95",
    "outputId": "3e230cbf-1f0d-4c8d-f2cd-90c6f3de6587"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       573\n",
      "           1       0.74      0.85      0.79        27\n",
      "\n",
      "   micro avg       0.98      0.98      0.98       600\n",
      "   macro avg       0.87      0.92      0.89       600\n",
      "weighted avg       0.98      0.98      0.98       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=(result.predict(X_test)>prob_threshold).apply(int)\n",
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "Wz6is-LwvP98",
    "outputId": "5918cd8e-1e6a-4c85-86c6-8a3d0d9410cf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predict-YES</th>\n",
       "      <th>Predict-NO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>565</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predict-YES  Predict-NO\n",
       "YES          565           4\n",
       "NO             8          23"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, y_pred),columns=['Predict-YES','Predict-NO'],index=['YES','NO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wZgi_pXGHD3E"
   },
   "source": [
    "### How do the probabilities compare between `Scikit-learn` and `Statsmodels` predictions? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O55cr8UrHqnM"
   },
   "outputs": [],
   "source": [
    "sklearn_prob = clf1.predict_proba(X_test)[...,1][:10]\n",
    "statsmodels_prob =  result.predict(X_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "colab_type": "code",
    "id": "KYumGbsAHDCp",
    "outputId": "5ec364f0-3999-43b6-8a4a-f150f9c8f071"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scikit-learn Prob</th>\n",
       "      <th>Statsmodels Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009529</td>\n",
       "      <td>0.026742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.000780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.375982</td>\n",
       "      <td>0.306287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.135925</td>\n",
       "      <td>0.132377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.016304</td>\n",
       "      <td>0.007412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.001065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.778033</td>\n",
       "      <td>0.874865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Scikit-learn Prob  Statsmodels Prob\n",
       "0           0.009529          0.026742\n",
       "1           0.001286          0.000780\n",
       "2           0.000001          0.000001\n",
       "3           0.375982          0.306287\n",
       "4           0.000704          0.000797\n",
       "5           0.135925          0.132377\n",
       "6           0.016304          0.007412\n",
       "7           0.001493          0.001065\n",
       "8           0.778033          0.874865\n",
       "9           0.000032          0.000161"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_comp_df=pd.DataFrame(data={'Scikit-learn Prob':list(sklearn_prob),'Statsmodels Prob':list(statsmodels_prob)})\n",
    "prob_comp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q_nNkT6NvP9-"
   },
   "source": [
    "### Coefficient interpretation\n",
    "\n",
    "What is the interpretation of the coefficient value for `age` and `FTI`?\n",
    "\n",
    "- With every one year of age increase, the log odds of the hypothyrodism **increases** by 0.0248 or the odds of hypothyroidsm increases by a factor of exp(0.0248) = 1.025 i.e. almost 2.5%.\n",
    "- With every one unit of FTI increase, the log odds of the hypothyrodism **decreases** by 0.1307 or the odds of hypothyroidsm decreases by a factor of exp(0.1307) = 1.1396 i.e. almost by 12.25%."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "q_nNkT6NvP9-"
   ],
   "name": "Logistic_Reg_sklearn_statsmodels.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
